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Semi-Supervised Algorithm with Knowledge-based Features for Learner's Profiles Interoperability

Ghorbel, Leïla and Zayani, Corinne Amel and Amous, Ikram and Sèdes, Florence Semi-Supervised Algorithm with Knowledge-based Features for Learner's Profiles Interoperability. (2018) International Journal of Technology Enhanced Learning, 10 (1/2). 137-159. ISSN 1753-5255

(Document in English)

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Official URL: https://www.doi.org/10.1504/IJTEL.2018.088343


Nowadays, the user can have several profiles found in different adaptive systems relative to various fields. In particular, adaptive e-learning systems respond to a strong need to adapt to each learner their proposed activities based on the data stored in his/her profile (learning-style, interest, etc.). However, each system can have incomplete data as far as the learner is concerned. Hence, the exchange of the learner's profile data is extremely important in order to enhance his/her learning experience. The exchange requires a matching process so as to resolve the large number of a learner's profiles differences whether in syntax, structure or semantics. In this context, we propose a matching process to automatically detect the similarity between the profile elements. The originality of this process resides in the fact that it rests on a new semi-supervised Tri-Training algorithm which significantly improves the state of the art approaches.

Item Type:Article
HAL Id:hal-02562112
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > Université de Sfax (TUNISIA)
Laboratory name:
Deposited On:29 Apr 2020 10:12

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